Predicting Clean Fluid Composition And Properties With A Rapid Formation Tester Pumpout

ABSTRACT

A method and system for estimating clean fluid composition and properties. A method may comprise disposing a downhole fluid sampling tool into a wellbore, wherein the downhole fluid sampling tool comprises optical instrumentation, obtaining a fluid sample with the downhole fluid sampling tool, wherein the fluid sample comprises a reservoir fluid contaminated with a well fluid, identifying input parameters from at least one sensor response on the optical instrumentation, and predicting a clean fluid sample of the reservoir fluid using an asymptote of a dimensional reduction analysis and equation of state. A system may comprise a downhole fluid sampling tool and a processing unit. The downhole fluid sampling tool may further comprise an optical instrumentation operable to obtain fluid samples of a reservoir fluid contaminated with a well fluid while the downhole fluid sampling tool is disposed in a wellbore.

BACKGROUND

During oil and gas exploration, many types of information may becollected and analyzed. The information may be used to determine thequantity and quality of hydrocarbons in a reservoir and to develop ormodify strategies for hydrocarbon production. For instance, theinformation may be used for reservoir evaluation, flow assurance,reservoir stimulation, facility enhancement, production enhancementstrategies, and reserve estimation. One technique for collectingrelevant information involves obtaining and analyzing fluid samples froma reservoir of interest. There are a variety of different tools that maybe used to obtain the fluid sample. The fluid sample may then beanalyzed to determine fluid properties, including, without limitation,component concentrations, plus fraction molecular weight, gas-oilratios, bubble point, dew point, phase envelope, viscosity, combinationsthereof, or the like. Conventional analysis has required transfer of thefluid samples to a laboratory for analysis. Downhole analysis of thefluid sample may also be used to provide real-time fluid properties,thus avoiding delays associated with laboratory analysis.

Accurate determination of fluid properties may be problematic as thefluid sample may often be contaminated with drilling fluids. Fluidsamples with levels of drilling fluid contamination may result innon-representative fluids and measured properties. Techniques todetermine drilling fluid contamination may include use of pump-outcurves, such as density, gas-to-oil ratio and resistivity, among otherproperties of the fluids. However, determination of drilling fluidcontamination using these techniques may be limited, for example, due tolack of significant decrease of the property value, non-linear behavioror properties to contamination levels, and unreliable propertymeasurements. To reduce drilling fluid contamination, longer pump-outtime may be required, which may lead to loss of rig time and increaserisk of stuck tools, among other problems.

BRIEF DESCRIPTION OF THE DRAWINGS

These drawings illustrate certain aspects of some of the embodiments ofthe present invention, and should not be used to limit or define theinvention;

FIG. 1 is a schematic diagram of an example downhole fluid sampling toolon a drill string;

FIG. 2 is a schematic diagram of an example downhole fluid sampling toolon a wireline;

FIG. 3 is a flow chart for predicting clean fluid samples; and

FIG. 4 is a chart illustrating a plot of Principle Component Scoresverse volume.

DETAILED DESCRIPTION

Disclosed herein are methods and systems for predicting contaminationlevel of a fluid sample from a downhole fluid sampling tool. Predictingfluid contamination (e.g., oil based drilling fluid contamination) maythen allow right time of sampling and determination of fluid propertiesof the reservoir fluid, even though the fluid sample was contaminatedwith well fluid, such as a drilling fluid or other well fluid introducedfrom the surface. As will be discussed in more detail below, the methodsand systems for fluid characterization may use a mathematical algorithmin conjunction with an equation of state to predict fluid contaminationand component concentrations of the reservoir fluid, among others.Inputs may include downhole measurements of the fluid sample, including,without limitation, C1, C2,C3,C45 weight percent, gas-oil ratio, oildensity (e.g., live oil or dead oil density), and/or bubble point.Inputs may further include a mud filtrate composition.

For example, due to overbalance pressure in the wellbore, drilling fluidinvades the formation in the vicinity of the wellbore during drilling.Unlike water-based mud (WBM), oil-based mud (OBM) is miscible with theformation fluid and therefore changes the composition and the propertiesof the original formation fluid. Before taking samples, the contaminatedfluid may be pumped out first. The pump out fluid may include mudfiltrate initially, but may become cleaner as the pumping progresses.The composition and properties measured by an optical sensor duringpumpout may be for contaminated fluid. Since OBM alters fluidproperties, fluid composition and properties may not be representativeof the original formation fluid, especially at the early pumpout stagewhen mud filtrate contamination level may be higher than 20 percent ofthe formation fluid. The Gas Oil Ratio “GOR” of the original formationfluid may be known to be higher than that of the contaminated fluid andthe bubble point pressure of the original formation fluid may be higherthan that of the contaminated fluid. Unless the contamination level maybe less than 5 percent of the formation fluid low, measured opticalfluid properties may be representative of a pure formation fluid. Thecomposition measured by downhole optical tools may be that ofcontaminated fluid because at the end of the pumpout, the contaminationlevel may be low but it may not be zero.

FIG. 1 is a schematic diagram is shown of downhole fluid sampling tool100 disposed on a drill string 102. The downhole fluid sampling tool 100may be used to obtain a fluid sample, for example, a fluid sample of areservoir fluid from subterranean formation 104. The reservoir fluid maybe contaminated with well fluid (e.g., drilling fluid) from wellbore106. As described herein, the fluid sample may be analyzed to determinefluid contamination and other fluid properties of the reservoir fluid.As illustrated, a wellbore 106 may extend through subterranean formation104. While the wellbore 106 is shown extending generally vertically intothe subterranean formation 104, the principles described herein are alsoapplicable to wellbores that extend at an angle through the subterraneanformation 104, such as horizontal and slanted wellbores. For example,although FIG. 1 shows a vertical or low inclination angle well, highinclination angle or horizontal placement of the well and equipment isalso possible. It should further be noted that while FIG. 1 generallydepicts a land-based operation, those skilled in the art will readilyrecognize that the principles described herein are equally applicable tosubsea operations that employ floating or sea-based platforms and rigs,without departing from the scope of the disclosure.

As illustrated, a drilling platform 108 may support a derrick 110 havinga traveling block 112 for raising and lowering drill string 102. Drillstring 102 may include, but is not limited to, drill pipe and coiledtubing, as generally known to those skilled in the art. A kelly 114 maysupport drill string 102 as it may be lowered through a rotary table116. A drill bit 118 may be attached to the distal end of drill string102 and may be driven either by a downhole motor and/or via rotation ofdrill string 102 from the surface 120. Without limitation, drill bit 118may include, roller cone bits, PDC bits, natural diamond bits, any holeopeners, reamers, coring bits, and the like. As drill bit 118 rotates,it may create and extend wellbore 106 that penetrates varioussubterranean formations 104. A pump 122 may circulate drilling fluidthrough a feed pipe 124 to kelly 114, downhole through interior of drillstring 102, through orifices in drill bit 118, back to surface 120 viaannulus 126 surrounding drill string 102, and into a retention pit 128.

Drill bit 118 may be just one piece of a downhole assembly that mayinclude one or more drill collars 130 and downhole fluid sampling tool100. Downhole fluid sampling tool 100, which may be built into the drillcollars 130) may gather measurements and fluid samples as describedherein. One or more of the drill collars 130 may form a tool body 132,which may be elongated as shown on FIG. 1. Tool body 132 may be anysuitable material, including without limitation titanium, stainlesssteel, alloys, plastic, combinations thereof, and the like. Downholefluid sampling tool 100 may further include one or more sensors 134 formeasuring properties of the fluid sample, reservoir fluid, wellbore 106,subterranean formation 104, or the like. The downhole fluid samplingtool 100 may be used to collect a fluid sample from subterraneanformation 104. As previously described, the fluid sample may comprise areservoir fluid, which may be contaminated with a well fluid. Thedownhole fluid sampling tool 100 may obtain and separately storedifferent fluid samples from subterranean formation 104. However,storing of the fluid samples in the downhole fluid sampling tool 100 maybe based on the determination of the fluid contamination. For example,if the fluid contamination exceeds a tolerance, then the fluid samplemay not be stored. If the fluid contamination is within a tolerance,then the fluid sample may be stored in the downhole fluid sampling tool100.

The downhole fluid sampling tool 100 may further include a fluidanalysis module 136. In examples, the fluid analysis module 136 maycomprise an optical instrumentation, such as an optical sensor, that maycontinuously monitor a reservoir fluid. The fluid analysis module 136may be operable to derive properties and characterize the fluid sample.By way of example, the fluid analysis module 136 may measure absorptionspectra and translate such measurements into component concentrations ofthe fluid sample, which may be lumped component concentrations, asdescribed above. The fluid analysis module 136 may also measuregas-to-oil ratio, live fluid density, live fluid viscosity, formationpressure, and formation temperature. The fluid analysis module 136 mayalso be operable to determine fluid contamination of the fluid sample.The fluid analysis module 136 include any instrumentality or aggregateof instrumentalities operable to compute, classify, process, transmit,receive, retrieve, originate, switch, store, display, manifest, detect,record, reproduce, handle, or utilize any form of information,intelligence, or data for business, scientific, control, or otherpurposes. For example, fluid analysis module 136 may include randomaccess memory (RAM), one or more processing units, such as a centralprocessing unit (CPU), or hardware or software control logic, ROM,and/or other types of nonvolatile memory.

Any suitable technique may be used for transmitting signals from thedownhole fluid sampling tool 100 to the surface 120. As illustrated, acommunication link 138 (which may be wired or wireless, for example) maybe provided that may transmit data from downhole fluid sampling tool 100to an information handling system 140 at surface 120. Informationhandling system 140 may include a processing unit 142, a monitor 144, aninput device 146 (e.g., keyboard, mouse, etc.), and/or computer media148 (e.g., optical disks, magnetic disks) that can store coderepresentative of the methods described herein. The information handlingsystem 140 may act as a data acquisition system and possibly a dataprocessing system that analyzes information from downhole fluid samplingtool 100. For example, information handling system 140 may process theinformation from downhole fluid sampling tool 100 for determination offluid contamination. The information handling system 140 may alsodetermine additional properties of the fluid sample (or reservoirfluid), such as component concentrations, pressure-volume-temperatureproperties (e.g., bubble point, phase envelop prediction, etc.) based onthe fluid characterization. This processing may occur at surface 120 inreal-time. Alternatively, the processing may occur at surface 120 oranother location after recovery of downhole fluid sampling tool 100 fromwellbore 106. Alternatively, the processing may be performed by aninformation handling system in wellbore 106, such as fluid analysismodule 136. The resultant fluid contamination and fluid properties maythen be transmitted to surface 120, for example, in real-time.

Referring now to FIG. 2, a schematic diagram is shown of downhole fluidsampling tool 100 on a wireline 150. As illustrated, wellbore 106 mayextend through subterranean formation 104. Downhole fluid sampling tool100 may be similar in configuration and operation to downhole fluidsampling tool 100 shown on FIG. 1 except that FIG. 2 shows downholefluid sampling tool 100 disposed on wireline 150. It should be notedthat while FIG. 2 generally depicts a land-based drilling system, thoseskilled in the art will readily recognize that the principles describedherein are equally applicable to subsea drilling operations that employfloating or sea-based platforms and rigs, without departing from thescope of the disclosure.

As illustrated, a hoist 152 may be used to run downhole fluid samplingtool 100 into wellbore 106. Hoist 152 may be disposed on a recoveryvehicle 154. Hoist 152 may be used, for example, to raise and lowerwireline 150 in wellbore 106. While hoist 152 is shown on recoveryvehicle 154, it should be understood that wireline 150 may alternativelybe disposed from a hoist 152 that is installed at surface 120 instead ofbeing located on recovery vehicle 154. Downhole fluid sampling tool 100may be suspended in wellbore 106 on wireline 150. Other conveyance typesmay be used for conveying downhole fluid sampling tool 100 into wellbore106, including coiled tubing and wired drill pipe, for example. Downholefluid sampling tool 100 may comprise a tool body 132, which may beelongated as shown on FIG. 1. Tool body 132 may be any suitablematerial, including without limitation titanium, stainless steel,alloys, plastic, combinations thereof, and the like. Downhole fluidsampling tool 100 may further include one or more sensors 134 formeasuring properties of the fluid sample, reservoir fluid, wellbore 106,subterranean formation 104, or the like. The downhole fluid samplingtool 100 may also include a fluid analysis module 136, which may beoperable to process information regarding fluid sample, as describedabove with respect to FIG. 1. The downhole fluid sampling tool 100 maybe used to collect fluid samples from subterranean formation 104. Thedownhole fluid sampling tool 100 may obtain and separately storedifferent fluid samples from subterranean formation 104.

As previously described, information from downhole fluid sampling tool100 may be transmitted to an information handling system 140, which maybe located at surface 120. As illustrated, communication link 138 (whichmay be wired or wireless, for example) may be provided that may transmitdata from downhole fluid sampling tool 100 to an information handlingsystem 140 at surface 120. Information handling system 140 may include aprocessing unit 142, a monitor 144, an input device 146 (e.g., keyboard,mouse, etc.), and/or computer media 148 (e.g., optical disks, magneticdisks) that may store code representative of the methods describedherein. In addition to, or in place of processing at surface 120,processing may occur downhole (e.g., fluid analysis module 136). Inexamples, information handling system 140 may perform computations toestimate clean fluid composition.

FIG. 3 shows a flow chart of an example of a method 300 for predictingclean fluid composition. The method 300 may be implemented using thesystems implemented on FIGS. 1 and 2, for example, predict clean fluidcomposition (e.g., drilling fluid contamination) of a fluid sample.Method 300 may be implemented at surface 120 or in wellbore 106. By wayof example, fluid analysis module 136 of downhole fluid sampling tool100 may include a processing unit (e.g., a microprocessor, etc.) thatmay be operable to implement one or more of the method steps of method300. By way of further example, information handling system 140 may alsoinclude a processing unit 142 (e.g., a microprocessor, etc.) that can beoperable to implement one or more of the method steps of method 156. Aswill be appreciated, processing may occur either in wellbore 106, atsurface 120, at a remote location, or a combination of these locations.

In step 302, fluid sample may be obtained. The fluid sample may be afluid sample from a reservoir of interest, for example, fromsubterranean formation 104 shown on FIGS. 1 and 2. Any suitabletechnique may be used to obtain fluid sample. As described previously,downhole fluid sampling tool 100 may be used to collect fluid sample ona drill string 102 (e.g., FIG. 1) or on a wireline 150 (e.g., FIG. 2),for example. For example, downhole fluid sampling tool 100 may beoperated to obtain a fluid sample. The fluid sample may be contaminatedwith a well fluid, such as drilled fluid. The fluid sample may beobtained at formation temperature and pressure. It should be understoodthat downhole fluid sampling tool 100 is merely illustrative of oneexample apparatus that may be used in obtaining a fluid sample and thoseof ordinary skill in the art should be able to select an appropriateapparatus and associated methodology to obtain a fluid sample. The fluidsample need not necessarily be collected downhole. By way of example,the techniques described herein may be used to characterize the fluidsample of a produced fluid that may be obtained at surface 120. Afterfluid sample is obtained, subsequent processing steps, discussed below,may occur at surface 120 or in wellbore 106. Alternatively, fluid samplemay be transferred to a remote location for one or more of thesubsequent processing steps.

In step 304, the fluid sample may be analyzed to derive input parametersthat characterize the fluid sample. Without limitation, the inputparameters may be obtained from measurements of the fluid sample. Themeasurements may be performed in wellbore 106, at surface 120, or at aremote location. The downhole fluid sampling tool 100 or other suitableformation evaluation tools may be used to analyze the fluid sample. Anymeasuring instrument capable of producing a measurable response to thechange of the fluid property may be used.

The measuring instrument may contain a detector and/or sensor detecting,for example, density, resistivity/conductivity, viscosity,chromatography, radioactivity, dielectric constant, optical density,magnetic resonance, weight, acoustic impedance, acoustic velocity,optical response, diffusion coefficients, molecular weight, refractiveindex at various wavelengths, and combinations thereof. One or moresensors or detectors (e.g. sensor 134 of downhole fluid sampling tool100 shown on FIG. 1) may be used in the measuring instrument.

The input parameters of the fluid sample that may be derived may includefluid properties that may be obtained from measurements of the fluidsample, including, without limitation, one or more of componentconcentrations (e.g., weight %, etc.), gas-to-oil ratio, live oildensity(or dead oil density) and bubble point. Additional fluidproperties that may be derived may include one or more of volumefraction of water, API gravity, live oil viscosity, formationtemperature, or formation pressure, among others. The componentconcentrations obtained from these measurements may typically be alumped component concentration with concentration of heavierhydrocarbons lumped together. By way of example, the componentconcentration may be provided showing fractions of carbon dioxide (CO₂),methane (CH4), ethane (C₂H₆), propane (C₃H₈), butane (C₄H₁₀), pentane(C₅H₁₂), and the C6+ group. The C6α group may include the concentrationof all hydrocarbons with six or more carbon atoms lumped into a singlecomponent concentration. In some instances, the C5 hydrocarbons may notbe separately reported, with the C5+0 hydrocarbon group lumped into asingle component concentration. Moreover, some of the lowerhydrocarbons, such as the C3, C4, or C5 hydrocarbons may also be groupedand reported together, for example, C3-C4 hydrocarbon group, C3-C5hydrocarbon group, and/or C4-C5 hydrocarbon group. These concentrationsmay be provided as weight or mole percentages. “Live oil” typicallyrefers to an oil at reservoir conditions. A fluid sample at reservoirconditions may be referred as “live oil.” The live oil density of thefluid sample may be obtained from measurements at reservoir conditions.Without limitation, the live oil density may be obtained using a densitysensor, for example, on downhole fluid sampling tool 100. The bubblepoint may include the temperature and pressure at which the first bubbleof gas comes out of the fluid sample. Without limitation, the bubblepoint may be obtained either from measurement or derived from empiricalcorrelation. Without limitation, the gas-to-oil ratio may be obtained bymeasuring the quantity of vapor component and liquid components of crudeoil using near infrared absorption peaks. The ratio of methane to theoil peak on a single phase live crude oil may be directly related togas-to-oil ratio.

Additional input parameters may also include mud filtrate composition.The term “mud filtrate” typically refers to the liquid part of adrilling fluid, for example, that passes through filter cake. Anysuitable method may be used to derive the mud filtrate composition,including a mud log.

In step 306, the composition of a clean fluid sample may be determined.The clean fluid sample composition may be determined by a mathematicalalgorithm. The composition determined in step 306 may be the compositionof a predicted clean fluid sample. In other words, the composition maythe composition of the reservoir fluid contaminated with the well fluid(e.g., drilling fluid). Without limitation, an algorithm to estimateclean fluid composition and properties may use an asymptote of aprincipal component analysis (PCA), which may allow for an equation ofstate to be developed. A principal component analysis is one method ofdimensional reduction and is linear in form. Other linear methods ofdimension reduction include singular value decomposition, also linear inform and wavelet analysis which is nonlinear in form. The disclosedmethod may be described by a principal component analysis, but it shouldbe understood that other methods of dimensional reduction may be usedincluding but not limited to those described. The inputs to thealgorithm may be optical responses of an integrated computationalelement (ICE). Each ICE core may be designed such that one particularfluid component or properties may be accentuated in the detectorresponse while muting all other additional information recorded bydownhole fluid sampling tool 100. In examples, sensor response signalsmay first be despiked using a filter and moving window averaging method.Data is first despiked using outlier detection filter method such asHampel filter and then moving average method is applied to the data.Window size is set for both filter method and moving average method.

Next, the sensor signals may be transformed by a Principal ComponentAnalysis (PCA).

The PCA reduces a high dimensional dataset, comprising more than 30individual data sets, to a low dimensional set, comprising 2 to 3individual data sets, of features. Dimensional data sets may be sensorsignals. Every sensor signal is a measurement of the optical response ofa particular fluid component or fluid property. The reduction may beperformed without loss, or error in the transmission of data, offinformation from the original dataset. In examples, a robust principalcomponent analysis (RPCA) may be chosen and unlike the classical PCA,RPCA may be resistant to outliers in a dataset, the dataset referring tooptical sensor data. The outliers identified by the RPCA may be excludedfrom the PCA scores and loadings. A robust regression method may also beapplied to PCA scores to compute scores fit as follows:

scores_(fit) =A+B Xν^(−β)  (1)

Where A and B are constants to be determined by a regression method. Thevariables A and B are determined using a constraint optimization method.The variable v is the formation fluid pumpout accumulated volume and βis the density decay rate and may be obtained by performing anoptimization on the density dataset using a nonlinear constrainedoptimization method. Similar to RPCA, robustfit regression method may beless sensitive to outliers in a dataset. The outliers identified by therobustfit regression method may also be excluded from the PCA scores andloadings. The asymptote of the fit to PCA scores shown in Equation (1)may be obtained as when accumulated volume, ν is infinitely large. As anexample, FIG. 4 shows a graph where the score of the first principalcomponent (PC1) and a fit for a dataset. The score is plotted againstvolume −β. At infinitely large volume, volume −β may be zero and aprojected value of the fit to a PC score when volume −β equals zero maybe used as the estimate of a clean sensor data set of the PC score.

Then the clean sensor data set may be inverted from an asymptote of thePCA scores fit and the PCA loadings. PCA loadings are the coefficientthat transforms the PCA scores (optical data in the PCA space) back tothe original dataset. The PCA score is a linear combination of theoriginal dataset. The coefficient that the scores are multiplied with toget the original dataset is the loadings. Using a machine-learningalgorithm, the concentration of the fluid components: methane (C1),ethane (C2), propane (C3), butane and pentane combined (C4-C5), and C6+may be estimated in-situ from the clean sensor data set. The molecularweight and the density of C6+ may be estimated by a predictive machinelearning method such as K-nearest neighbor (“KNN”). The live oildensity, GOR, and bubble point pressure may be computed using the cleanfluid composition and cubic equation of state.

Referring back to FIG. 3, in step 308, equation of state calculationsmay be performed to derive calculated clean fluid properties. Equationof state flash calculations may be used to derive the gas to oil ratioand dead oil density, among other fluid properties, of the fluid sample.Equation of state calculations may be used to derive the phase envelopeand bubble point of the fluid sample, among other properties. To reducethe computational complexity of the equation of state calculations, thecomponent mole fractions of the fluid sample may be lumped, for example,to C1, C2, . . . , C34, C35, and C36+ mole fractions. The calculatedfluid properties determined by the equation of state flash calculationsmay include one or more of gas-to-oil ratio, dead oil density, bubblepoint, and/or phase envelope, for example. “Dead oil” typically refersto an oil at sufficiently low pressure that it contains substantially nodissolved gas or relatively thick oil that has lost its volatilecomponents. Additional calculated fluid properties may include, withoutlimitation, liquid mole fraction distribution, vapor mole fractiondistribution, the density, molecular weight and mole volume for theliquid and vapor portion of the live oil.

The equation of state calculations may be based on equation of stateequations that represent the functional relationship between pressure,volume, and temperature of the fluid sample. Equations of states may beused to predict physical properties, such as macroscopicpressure-volume-temperature properties, including bubble point, dewpoint, phase envelope, viscosity, gas-to-oil ratio, density,combinations thereof.

Equation of state calculations may use information or properties such astemperature, pressure, and composition. For example, one simple equationof state is PV=nRT, known as the ideal gas law, where P=pressure,V=volume, n=moles, R=Ideal Gas Constant (also used for unitsconversion), and T=absolute temperature (Kelvin or Rankine). When thephysical properties and composition of the reservoir fluid under a givenset of conditions are known, the behavior of the reservoir fluid atother pressures and temperatures may be predicted. Equations of statethat may be used may include, for example, expansions of the ideal gaslaw to account for individual molecular compositions. According to someembodiments, they are third order equations. Any of a variety ofequations of state may be used. The equation of state may be cubic ornon-cubic. The equation of state may vary depending on or morecompositional components of the fluid sample. The equations of statehave many names, as they have been modified to improve the match betweenpredicted and observed behavior. Without limitation, the equation ofstate may be selected from one or more of Boyle, Van der Waals,Redlich-Kwong, Soave-Redlich-Kwong, Peng-Robinson,Peng-Robinson-Stryjek-Vera, Patek-Teja, Schmit-Wenzel, orEsmaeilzadeh-Roshanfekr.

TABLE 1 End Volume Statistics (L) C1(g/cc) C2(g/cc) C3(g/cc) C4C5(g/cc)C6(g/cc) LOD(g/cc) GOR(scf/bbl) Bubblepoint(psi) 198 0.08954 0.000150.0009 0.01745 0.53262 0.71038 1078.55 2466.66 150 0.0910 0.000160.00088 0.01842 0.52998 0.70687 1103 2487.87 100 0.09332 0.00017 0.000840.01961 00.52848 0.70163 1129.2 2493.81 50 0.09492 0.00013 0.000960.01566 0.5334 0.70167 1139.96 2550.8 Relative 2.581866 11.198855.586592 9.38712917 0.43096285 0.60617958 2.475281096 1.438171192Standard Deviation % 5.667931 −15.3846 6.25 −11.430396 0.14623172−1.2413243 5.387031124 3.298572997 difference between 198 L and 50 L

Table 1 is the predicted clean fluid and properties at different pumpoutend volumes. As seen in Table 1, the clean fluid composition andproperties predicted at pumpout end volume of fifty (50) liters may notsignificantly differ from those predicted at one hundred and ninetyeight (198) liters. Additionally, analysis of PC1 shows that most of thevariation in the optical data occurs within the first few tens of litersduring pumpout. This may allow an operator to determine when and whereto draw clean samples in a less time and with less pumpout.

After collecting clean samples with downhole fluid sampling tool 100(e.g., FIG. 1 or FIG. 2). An operator may make a production orcompletion decision based at least in part on the measurements andinformation collected from the clean samples. For example, production orcompletion decisions may be choosing material used in Facilities design[H₂S, gas/oil ratio (GOR), corrosion, and scaling], determining flowassurance remediation strategy (i.e., introduction of hydrate, wax,asphaltene into a wellbore), creating reservoir simulation input (EOSmodeling), and determining enhanced oil recovery based on a fluid type.

Distinctive improvements over current methods may be the ability toestimate clean fluid compositions, such as methane (C1), ethane (C2),propane (C3), butane and pentane combined (C4-C5), and C6+ using PCAasymptote in real-time. Additional improvements may allow for usingclean fluid composition and an equation of state to estimate bubblepoint and GOR at downhole condition in real time. Current methodsmeasure few discreet bubble points of contaminated fluid while theproposed method, disclosed above, may predict clean fluid bubble pointat pumpout end volume as low as forty (40) liters. Clean bubble pointprovides a pumping rate limit to maintain reservoir fluid in monophasiccondition. Currently, optical tools may predicts composition and someproperties of contaminated fluid. This solution may enable an operatorto predict clean fluid composition and properties such as GOR and bubblepoint in real time.

This may be possible due to clean fluid bubble points may be higher thanthat of contaminated fluid. Clean fluid bubble points provide the limitfor a pumping rate during pumpout. Improvements may also allow for anability to predict formation composition at low pumpout end volume evenbefore taking samples. This enables an operator to perform rapid pumpout(pumping fluid for as short as 20-25 mins) at different depths for thepurpose of determining reservoir continuity.

The preceding description provides various embodiments of systems andmethods of use which may contain different method steps and alternativecombinations of components. It should be understood that, althoughindividual embodiments may be discussed herein, the present disclosurecovers all combinations of the disclosed embodiments, including, withoutlimitation, the different component combinations, method stepcombinations, and properties of the system.

Statement 1. A method for estimating clean fluid composition andproperties may comprise disposing a downhole fluid sampling tool into awellbore, wherein the downhole fluid sampling tool comprises opticalinstrumentation, obtaining a fluid sample with the downhole fluidsampling tool, wherein the fluid sample comprises a reservoir fluidcontaminated with a well fluid, identifying input parameters from atleast one sensor response on the optical instrumentation, wherein theinput parameters comprise fluid properties obtained from measurement ofthe fluid sample and mud filtrate composition, and predicting a cleanfluid sample of the reservoir fluid using an asymptote of a dimensionalreduction analysis and equation of state.

Statement 2. The method of statement 1, further comprising despiking theat least one sensor response.

Statement 3. The method of statements 1 or 2, wherein the despiking anoriginal dataset is performed by a Hampel filter.

Statement 4. The method of statements 1 to 3, further comprisingtransforming the at least one sensor response by a principal componentanalysis.

Statement 5. The method of statements 1 to 4, further comprisingperforming a robust principal component analysis on the at least onesensor response.

Statement 6. The method of statements 1 to 5, further comprisingapplying a robust regression method on the at least one sensor response,wherein the robust regression method is computed as scores_(fit)=A+B Xν^(−β) where A and B are constants determined by a regression method, νis a formation fluid pumpout accumulated volume, and μ is a densitydecay rate.

Statement 7. The method of statements 1 to 6, further comprisingperforming a production or completion decision.

Statement 8. The method of statements 1 to 7, wherein the well fluidcomprises a drilling fluid.

Statement 9. The method of statements 1 to 8, wherein obtaining thefluid sample comprising operating the downhole fluid sampling tool inthe wellbore to obtain the fluid sample.

Statement 10. A method to estimate clean fluid composition may comprisedespiking an original dataset, applying a robust regression method on atleast one sensor response, wherein the robust regression method iscomputed as scores_(fit)=A+B X ν^(−β), where A and B are constantsdetermined by a regression method, ν is a formation fluid pumpoutaccumulated volume, and β is a density decay rate, removing acontaminated reservoir fluid, and performing a pumpout to remove cleanreservoir fluid from the wellbore.

Statement 11. The method of statement 10, wherein the despiking theoriginal dataset is performed by a Hampel filter.

Statement 12. The method of statements 10 or 11, further comprisingperforming a robust principal component analysis on the original dataset.

Statement 13. The method of statements 10 to 12, further comprisingtransforming the original data set by a principal component analysis.

Statement 14. The method of statements 10 to 13, further comprisingfitting an asymptote to the clean sensor data set.

Statement 15. A system for estimating clean fluid composition maycomprise a downhole fluid sampling tool comprising opticalinstrumentation operable to obtain fluid samples of a reservoir fluidcontaminated with a well fluid while the downhole fluid sampling tool isdisposed in a wellbore, and a processing unit configured to: predict aclean fluid sample of the reservoir fluid from at least one sensorresponse on the optical instrumentation using an asymptote of aprincipal component analysis and equation of state.

Statement 16. The system of statement 15, wherein the processing unit isfurther configured to despike the at least one sensor response.

Statement 17. The system of statement 16, wherein a Hampel filter isused to despike the at least one sensor response.

Statement 18. The system of statements 15 to 17, wherein the processingunit is further configured to transform the at least one sensor responseto a principal component analysis space.

Statement 19. The system of statements 15 to 18, wherein the processingunit is further configured to perform a robust principal componentanalysis on the at least one sensor response.

Statement 20. The system of statements 15 to 19, wherein the processingunit is further configured to apply a robust regression method on the atleast one sensor response, wherein the robust regression method iscomputed as scores_(fit)=A+B X ν^(−β), where A and B are constantsdetermined by a regression method, ν is a formation fluid pumpoutaccumulated volume, and β is a density decay rate.

It should be understood that the compositions and methods are describedin terms of “comprising,” “containing,” or “including” variouscomponents or steps, the compositions and methods can also “consistessentially of” or “consist of” the various components and steps.Moreover, the indefinite articles “a” or “an,” as used in the claims,are defined herein to mean one or more than one of the element that itintroduces.

Therefore, the present embodiments are well adapted to attain the endsand advantages mentioned as well as those that are inherent therein. Theparticular embodiments disclosed above are illustrative only, as thepresent invention may be modified and practiced in different butequivalent manners apparent to those skilled in the art having thebenefit of the teachings herein. Although individual embodiments arediscussed, the invention covers all combinations of all thoseembodiments. Furthermore, no limitations are intended to the details ofconstruction or design herein shown, other than as described in theclaims below. Also, the terms in the claims have their plain, ordinarymeaning unless otherwise explicitly and clearly defined by the patentee.It is therefore evident that the particular illustrative embodimentsdisclosed above may be altered or modified and all such variations areconsidered within the scope and spirit of the present invention. Ifthere is any conflict in the usages of a word or term in thisspecification and one or more patent(s) or other documents that may beincorporated herein by reference, the definitions that are consistentwith this specification should be adopted.

What is claimed is:
 1. A method for estimating clean fluid compositionand properties, comprising: disposing a downhole fluid sampling toolinto a wellbore, wherein the downhole fluid sampling tool comprisesoptical instrumentation; obtaining a fluid sample with the downholefluid sampling tool, wherein the fluid sample comprises a reservoirfluid contaminated with a well fluid; identifying input parameters fromat least one sensor response on the optical instrumentation, wherein theinput parameters comprise fluid properties obtained from measurement ofthe fluid sample and mud filtrate composition; and predicting a cleanfluid sample of the reservoir fluid using an asymptote of a dimensionalreduction analysis and equation of state.
 2. The method of claim 1,further comprising despiking the at least one sensor response.
 3. Themethod of claim 2, wherein the despiking an original dataset isperformed by a Hampel filter.
 4. The method of claim 1, furthercomprising transforming the at least one sensor response by a principalcomponent analysis.
 5. The method of claim 1, further comprisingperforming a robust principal component analysis on the at least onesensor response.
 6. The method of claim 5, further comprising applying arobust regression method on the at least one sensor response, whereinthe robust regression method is computed as scores_(fit)=A+B X ν^(−β),where A and B are constants determined by a regression method, ν is aformation fluid pumpout accumulated volume, and β is a density decayrate.
 7. The method of claim 1, further comprising performing aproduction or completion decision.
 8. The method of claim 1, wherein thewell fluid comprises a drilling fluid.
 9. The method of claim 1, whereinobtaining the fluid sample comprising operating the downhole fluidsampling tool in the wellbore to obtain the fluid sample.
 10. A methodto estimate clean fluid composition, comprising: despiking an originaldataset, applying a robust regression method on at least one sensorresponse, wherein the robust regression method is computed asscores_(fit)=A+B X ν^(−β), where A and B are constants determined by aregression method, ν is a formation fluid pumpout accumulated volume,and β is a density decay rate; removing a contaminated reservoir fluid;and performing a pumpout to remove clean reservoir fluid from thewellbore.
 11. The method of claim 10, wherein the despiking the originaldataset is performed by a Hampel filter.
 12. The method of claim 10,further comprising performing a robust principal component analysis onthe original data set.
 13. The method of claim 10, further comprisingtransforming the original data set by a principal component analysis.14. The method of claim 10, further comprising fitting an asymptote tothe clean sensor data set.
 15. A system for estimating clean fluidcomposition, comprising: a downhole fluid sampling tool comprisingoptical instrumentation operable to obtain fluid samples of a reservoirfluid contaminated with a well fluid while the downhole fluid samplingtool is disposed in a wellbore; and a processing unit configured to:predict a clean fluid sample of the reservoir fluid from at least onesensor response on the optical instrumentation using an asymptote of aprincipal component analysis and equation of state.
 16. The system ofclaim 15, wherein the processing unit is further configured to despikethe at least one sensor response.
 17. The system of claim 16, wherein aHampel filter is used to despike the at least one sensor response. 18.The system of claim 15, wherein the processing unit is furtherconfigured to transform the at least one sensor response to a principalcomponent analysis space.
 19. The system of claim 15, wherein theprocessing unit is further configured to perform a robust principalcomponent analysis on the at least one sensor response.
 20. The systemof claim 15, wherein the processing unit is further configured to applya robust regression method on the at least one sensor response, whereinthe robust regression method is computed as scores_(fit)=A+B X ν^(−β),where A and B are constants determined by a regression method, ν is aformation fluid pumpout accumulated volume, and β is a density decayrate. 5